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Senior LLM & ML Engineer (100% REMOTE)

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FARMINGTON HILLS, MI(remote)

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JOB DETAILS
JOB TYPE
Full-time, Employee
SKILLS
A/B Testing, Adtech, Advertising, Amazon Web Services (AWS), Analysis Skills, Artificial Intelligence (AI), Best Practices, Cloud Computing, Communication Skills, Continuous Deployment/Delivery, Cross-Functional, Data Management, Data Sets, Deep Learning, GCP (Good Clinical Practices), GPU (Graphics Processing Unit), Machine Learning, Machine Tool, Marketing, Marketing Campaign, Marketing Strategy, Memory Hardware, Microsoft Windows Azure, Modeling Languages, Natural Language Processing (NLP), Open Source, Production Systems, Prototyping, React.js, Realtime Operating System, Startup, Use Cases
LOCATION
FARMINGTON HILLS, MI
POSTED
14 days ago
Senior/Principal LLM & ML Engineer
(100% REMOTE)
Full Time Role
 
As a Senior/Principal LLM & ML Engineer, you’ll be at the forefront of building our core intelligence stack. You will lead the design and implementation of cutting-edge machine learning systems, with a strong emphasis on LLMs/SLMs, agentic-based architectures, and real-time inference to power next-gen marketing and advertising workflows.
 
ESSENTIAL FUNCTIONS AND RESPONSIBILITIES
  • As a Senior/Principal LLM & ML Engineer, you’ll play a foundational role in:
  • Architect and implement scalable LLM/GenAI systems optimized for MarTech/AdTech use cases (e.g., content generation, sentiment analysis, resonance analysis, recommended marketing strategies, campaign optimization, audience segmentation, personalization, targeting, etc.)
  • Develop and fine-tune language models (LLMs/SLMs) for context-specific using open-source and proprietary datasets for use cases such as entity recognition, intent classification, recommendation, and ranking.
  • Designing and deploying agentic systems (multi-agent pipelines using frameworks such as LangGraph, AutoGen, or CrewAI)
  • Maintain best practices for model evaluation, A/B testing, and continuous learning in real-time production environments.
  • Develop scalable training and inference infrastructure leveraging multi-GPU environments (A100/H100 preferred).
  • Building robust data pipelines and model training loops that support rapid experimentation and deployment.
  • Contribute to LLMOps practices, including model monitoring, evaluation, and continuous deployment.
  • Collaborating with product, data, and engineering teams to turn AI prototypes into scalable production services.
  • Rapidly prototype research-backed features by translating research papers into working code.
 
KNOWLEDGE, SKILLS, ABILITIES, AND QUALIFICATIONS
  • 6+ years of experience in ML/AI roles, with at least 2+ years building with LLMs/SLMs
  • Strong experience in Deep Learning & Natural Language Processing (PyTorch, or TensorFlow)
  • Hands-on experience with fine-tuning foundation models (LLaMA, Mistral, GPT-like models) and deploying inference pipelines.
  • Proficiency with Hugging Face and popular LLM training & fine-tuning techniques (LoRA, PEFT, etc.)
  • Experience with Vector/Semantic search, RAG pipelines, or embedding optimization (Pinecone, FAISS, Redis Vector)
  • Familiarity with agent-based frameworks (LangGraph, ReAct, AutoGen, CrewAI, LangChain Agents, etc.).
  • Experience with LLMOps tooling and frameworks (Weights & Biases, MLflow, Prompt Layer, etc.)
  • Experience deploying ML systems in cloud environments (AWS, GCP, Azure) and using MLOps tools.
  • Understanding of GPU/memory optimization, distributed training, and batch in strategies
  • Strong software engineering skills (Python, APIs, microservices, containerization with Docker/K8s)
  • Strong communication skills and ability to thrive in fast-paced, cross-functional teams.
  • A major plus is knowledge of MarTech/AdTech data pipelines, targeting, or attribution models.
  • Experience with real-time personalization systems or ad-serving infrastructure.
  • Contributions to open-source LLM frameworks or research papers.
  • Prior startup or growth-stage company experience

About the Company

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